Research Proposal
A roadmap to optimization of the operation of the demand planning department and process for a positive financial effect.
Research Questions
- How can demand planning to support a better decision-making process?
- How can demand planning help planners work more efficiently?
- How can demand planning increase agility to react and process unpredicted events?
- What is the strategic value of demand planning?
Research Aim
To create a roadmap of excellent operation of the demand planning department for the realization of a positive financial effect in the long run.
Research Objectives
- To explore the various available methods of operation for the demand planning department.
- To establish ways in which technology can be incorporated into demand planning for quality outcomes.
Reasoned Justification of the Research
This research seeks to establish an optimal way of operating the demand planning department to realize a seamless flow of business in an organization and a positive financial effect. The demand planning department is one of the key departments in a business. It is tasked with analyzing sales, consumer trends, and seasonality data to help a business meet its customers’ demands conveniently, efficiently, and on a timely basis (Yang & Yu, 2019, p. 55). Demand planning involves various departments in a business, such as the sales department, the manufacturing department, and the inventory department. All these departments work together to forecast future demand. Excess inventory is also a potential risk to the business and locks up working capital that would have been used to generate more profits. Therefore, demand planning helps to maintain proper business liquidity as much as possible. Demand planners need to be well informed about the steps involved in demand planning. These steps include the following:
- Creation of a team.
- A good demand planning team should be cross-departmental, with every member having clear roles and responsibilities depending on his or her area of expertise.
- Selection of the relevant internal data.
- The demand planning team should agree and gather information and internal data for the development of an accurate forecast.
- Enhancing demand planning with external data.
- This includes overall market conditions and shifts in the market.
- Collaborative development of a statistical forecast.
- This can be achieved through the help of statistical software such as the SPSS.
- Review of the demand forecast.
- This ensures that the demand planning is in agreement with broader business financial forecasts.
- Weigh forecasts against stocks.
- This ensures that any potential bottlenecks in the supply chain are well prepared for. It involves checking with all stakeholders about their preparedness in accordance with the demand forecast.
Demand planning ensures customer satisfaction and profitability for the business. It facilitates proper decision-making in business operations, striking a balance between customer demand and inventory levels (Yu et al., 2019, p. 186). Accurate demand planning is important and critical to realizing an efficient supply chain for inventory and ultimately maximizing profitability (Hofmann & Rutschmann, 2018, pp. 15). Demand planning is a continuous process aimed at responding to the market. To achieve excellent performance of a business, management must come up with various ways of optimally operating the demand planning department.
The department may be assigned to an individual who is a demand planning expert to work on it exclusively. On the other hand, the demand planning department may be run by a team of experts from various departments to ensure a positive financial outcome (Bellisario & Pavlov, 2018, p. 373). Technology has become a severe incentive for business activity; integration into IT would be an essential step toward achieving business success.
Limitations of the Study
While this study has many important contributions, the obtained results should be considered within the context of limitations. Firstly, the study only examines how the demand planning department impacts a business without considering the manufacturing system. Without a well-functioning manufacturing system, demand planning will be of no importance (Uzsoy et al., 2018, pp. 4551). For example, the rapid change in the pace of industrial requirements due to the extreme evolution of technology makes it necessary for quick investigation of potential system alternatives towards a more competitive manufacturing system design (Liu et al., 2018, pp. 1043). Demand planning mainly focuses on the equilibrium of market demand and supply chain (Greenstone et al., 2020, p. 210). It does not explain how to create order, assuming that it does not exist.
Relevant Theme and Discipline/Theory Area
Change management is an organized transformation of the process and goals of an organization. A very good example of change management is demand planning. It is mostly achieved through product portfolio management and statistical forecasting. Product portfolio management is a study that explains a product life cycle in the market from the time a product is introduced in the market to its end of life (Shambaugh, 2018, p. 93). A product may experience two very diverse outcomes during its introduction to the market. A product may enjoy very high demand in its first days on the market or very low demand depending on the economic times.
Statistical forecasting is a discipline that seeks to forecast supply chains with advanced statistical algorithms (Seyedan & Mafakheri, 2020, p. 15). Demand planning and supply chains are intertwined due to the fact that they both aim at conveniently and efficiently serving customers to their satisfaction. Accurate statistical forecasting leads to proper demand planning, thus enabling a business to avoid losses occasioned by a lack of equilibrium between market demand and supply.
Types of Data Employed
This research has used a mix of both secondary and primary data. The majority of the primary data used came from sample financial statements and financial ratios from various local businesses. Simple random sampling was used in obtaining the financial statements. This data has also been sourced from interviews with various focus business groups. The secondary data used has been sourced from publications of well-renowned business institutions such as the Institute of Business Management and individual business and economics scholars.
Method of Data Collection
This research has exclusively used interviews as the main method of data collection. Business executives that are involved in the day-to-day running of their respective businesses’ operations and management, including demand planning and supply chain management, were the main focus. The reason is these people are with firsthand information about the demand planning process and its bottlenecks. They also have the express authority to engage third parties in the scrutinization of their respective businesses without conflict of interest (Yang, 2021, p. 401). For interviewing as a method of data collection to be successful, researchers have to assure the owners of the data of the privacy of their data as well as protect the data from landing in unsafe hands.
Preliminary Hypothesis (null H0 and alternative H1)
There is a connection between financial statements and financial ratios used in the quantitative research method used to collect data in this research. Financial ratios are calculated from financial statements.
Direction for Further Research
Further research ought to focus on how demand planning can be used to increase the market for the products of a business and not only look at the equilibrium of market demand and the supply chain. Technology is the next frontier in business operations, such as demand planning. Further research should try to focus on the various available methods of incorporating technology in demand planning and statistical forecasting.
Reference List
Bellisario, A., & Pavlov, A. (2018). ‘Performance management practices in lean manufacturing organizations: a systematic review of research evidence.’ Production Planning & Control, 29(5), pp. 367-385.
Greenstone, M., Mas, A., & Nguyen, H. L. (2020). ‘Do credit market shocks affect the real economy? Quasi-experimental evidence from the great recession and “normal” economic times,’ American Economic Journal: Economic Policy, 12(1), pp. 200-225.
Hofmann, E., & Rutschmann, E. (2018). ‘Big data analytics and demand forecasting in supply chains: a conceptual analysis,’ The International Journal of Logistics Management, 29(2), pp. 1-28.
Liu, Y., Chen, Y., & Yang, G. (2018). ‘Developing multiobjective equilibrium optimization method for sustainable uncertain supply chain planning problems,’ IEEE Transactions on Fuzzy Systems, 27(5), 1037-1051.
Seyedan, M., & Mafakheri, F. (2020). ‘Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities,’ Journal of Big Data, 7(1), pp. 1-22.
Shambaugh, D. (2018). ‘US-China rivalry in Southeast Asia: power shift or competitive coexistence?’ International Security, 42(4), pp. 85-127.
Uzsoy, R., Fowler, J. W., & Mönch, L. (2018). ‘A survey of semiconductor supply chain models Part II: demand planning, inventory management, and capacity planning,’ International Journal of Production Research, 56(13), pp. 4546-4564.
Yang, J., & Yu, K. (2019). ‘The role of an integrated logistics and procurement service offered by a 3PL firm in supply chain,’ Journal of Management Analytics, 6(1), pp. 49-66.
Yang, M. J. (2021). ‘The interdependence imperative: business strategy, complementarities, and economic policy,’ Oxford Review of Economic Policy, 37(2), pp. 392-415.
Yu, W., Dillon, T., Mostafa, F., Rahayu, W., & Liu, Y. (2019). ‘A global manufacturing big data ecosystem for fault detection in predictive maintenance,’ IEEE Transactions on Industrial Informatics, 16(1), pp. 183-192.